SOTAVerified

Quantization

Quantization is a promising technique to reduce the computation cost of neural network training, which can replace high-cost floating-point numbers (e.g., float32) with low-cost fixed-point numbers (e.g., int8/int16).

Source: Adaptive Precision Training: Quantify Back Propagation in Neural Networks with Fixed-point Numbers

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2376–2400 of 4925 papers

TitleStatusHype
Deep neural networks algorithms for stochastic control problems on finite horizon: convergence analysis—0
Killing Two Birds with One Stone: Quantization Achieves Privacy in Distributed Learning—0
Learning Kernel-Modulated Neural Representation for Efficient Light Field Compression—0
K-Means Hashing: An Affinity-Preserving Quantization Method for Learning Binary Compact Codes—0
Knowledge Distillation: A Survey—0
Knowledge Distillation in Vision Transformers: A Critical Review—0
Knowledge Transfer in Model-Based Reinforcement Learning Agents for Efficient Multi-Task Learning—0
Learning Linear Block Codes with Gradient Quantization—0
Designing a Classifier for Active Fire Detection from Multispectral Satellite Imagery Using Neural Architecture Search—0
Kramers-Kronig Receiver Combined With Digital Resolution Enhancer—0
KurTail : Kurtosis-based LLM Quantization—0
Integer Scale: A Free Lunch for Faster Fine-grained Quantization of LLMs—0
KV Cache is 1 Bit Per Channel: Efficient Large Language Model Inference with Coupled Quantization—0
KVmix: Gradient-Based Layer Importance-Aware Mixed-Precision Quantization for KV Cache—0
Deep neural networks algorithms for stochastic control problems on finite horizon: numerical applications—0
Deep Neural Network Models Compression—0
L1-Norm Batch Normalization for Efficient Training of Deep Neural Networks—0
L4Q: Parameter Efficient Quantization-Aware Fine-Tuning on Large Language Models—0
Integer or Floating Point? New Outlooks for Low-Bit Quantization on Large Language Models—0
A Wave is Worth 100 Words: Investigating Cross-Domain Transferability in Time Series—0
LAMBDA: Covering the Solution Set of Black-Box Inequality by Search Space Quantization—0
LANCE: Efficient Low-Precision Quantized Winograd Convolution for Neural Networks Based on Graphics Processing Units—0
Design of Stochastic Quantizers for Privacy Preservation—0
A Low Memory Footprint Quantized Neural Network for Depth Completion of Very Sparse Time-of-Flight Depth Maps—0
Learning Sparse Low-Precision Neural Networks With Learnable Regularization—0
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All datasetsImageNetCIFAR-10Wiki-40BAgeDB-30CFP-FPCOCO (Common Objects in Context)IJB-BIJB-CKnowledge-based:LFW

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1FQ-ViT (ViT-L)Top-1 Accuracy (%)85.03—Unverified
2FQ-ViT (ViT-B)Top-1 Accuracy (%)83.31—Unverified
3FQ-ViT (Swin-B)Top-1 Accuracy (%)82.97—Unverified
4FQ-ViT (Swin-S)Top-1 Accuracy (%)82.71—Unverified
5FQ-ViT (DeiT-B)Top-1 Accuracy (%)81.2—Unverified
6FQ-ViT (Swin-T)Top-1 Accuracy (%)80.51—Unverified
7FQ-ViT (DeiT-S)Top-1 Accuracy (%)79.17—Unverified
8Xception W8A8Top-1 Accuracy (%)78.97—Unverified
9ADLIK-MO-ResNet50-W4A4Top-1 Accuracy (%)77.88—Unverified
10ADLIK-MO-ResNet50-W3A4Top-1 Accuracy (%)77.34—Unverified
#ModelMetricClaimedVerifiedStatus
13DCNN_VIVA_3MAP160,327.04—Unverified
2DTQMAP0.79—Unverified
#ModelMetricClaimedVerifiedStatus
1OutEffHop-Bert_basePerplexity6.3—Unverified
2OutEffHop-Bert_basePerplexity6.21—Unverified
#ModelMetricClaimedVerifiedStatus
1Accuracy98.13—Unverified
#ModelMetricClaimedVerifiedStatus
1Accuracy92.92—Unverified
#ModelMetricClaimedVerifiedStatus
1SSD ResNet50 V1 FPN 640x640MAP34.3—Unverified
#ModelMetricClaimedVerifiedStatus
1TAR @ FAR=1e-495.13—Unverified
#ModelMetricClaimedVerifiedStatus
1TAR @ FAR=1e-496.38—Unverified
#ModelMetricClaimedVerifiedStatus
13DCNN_VIVA_5All84,809,664—Unverified
#ModelMetricClaimedVerifiedStatus
1Accuracy99.8—Unverified